Precise Discrimination for Multiple Etiologies of Dementia Cases Based on Deep Learning with Electroencephalography.

INTRODUCTION: It is critical to develop accurate and universally available biomarkers for dementia diseases to appropriately deal with the dementia problems under world-wide rapid increasing of patients with dementia. In this sense, electroencephalography (EEG) has been utilized as a promising examination to screen and assist in diagnosing dementia, with advantages of sensitiveness to neural functions, inexpensiveness, and high availability. Moreover, the algorithm-based deep learning can expand EEG applicability, yielding accurate and automatic classification easily applied even in general hospitals without any research specialist. METHODS: We utilized a novel deep neural network, with which high accuracy of discrimination was archived in neurological disorders in the previous study. Based on this network, we analyzed EEG data of healthy volunteers (HVs, N = 55), patients with Alzheimer's disease (AD, N = 101), dementia with Lewy bodies (DLB, N = 75), and idiopathic normal pressure hydrocephalus (iNPH, N = 60) to evaluate the discriminative accuracy of these diseases. RESULTS: High discriminative accuracies were archived between HV and patients with dementia, yielding 81.7% (vs. AD), 93.9% (vs. DLB), 93.1% (vs. iNPH), and 87.7% (vs. AD, DLB, and iNPH). CONCLUSION: This study revealed that the EEG data of patients with dementia were successfully discriminated from HVs based on a novel deep learning algorithm, which could be useful for automatic screening and assisting diagnosis of dementia diseases.

Language-related cerebral oscillatory changes are influenced equally by genetic and environmental factors.

Twin studies have suggested that there are genetic influences on inter-individual variation in terms of verbal abilities, and candidate genes have been identified by genome-wide association studies. However, the brain activities under genetic influence during linguistic processing remain unclear. In this study, we investigated neuromagnetic activities during a language task in a group of 28 monozygotic (MZ) and 12 dizygotic (DZ) adult twin pairs. We examined the spatio-temporal distribution of the event-related desynchronizations (ERDs) in the low gamma band (25-50Hz) using beamformer analyses and time-frequency analyses. Heritability was evaluated by comparing the respective MZ and DZ correlations. The genetic and environmental contributions were then estimated by structural equation modeling (SEM). We found that the peaks of the low gamma ERDs were localized to the left frontal area. The power of low gamma ERDs in this area exhibited higher similarity between MZ twins than that between DZ twins. SEM estimated the genetic contribution as approximately 50%. In addition, these powers were negatively correlated with the behavioral verbal scores. These results improve our understanding of how genetic and environmental factors influence cerebral activities during linguistic processes.

Respiratory Function Under Intrathecal Baclofen Therapy in Patients With Spastic Tetraplegia.

OBJECTIVES: Intrathecal baclofen (ITB) therapy is an effective treatment for patients with severe spasticity. However, the effect of ITB therapy on respiratory function has not been reported in detail. In this study we quantitatively analyzed the effects of ITB on the respiratory function of patients with spastic tetraplegia. METHODS: We retrospectively reviewed 23 patients who were administrated ITB therapy from January 2009 to December 2012. Six of these 23 patients, who had spastic tetraplegia and were able to undergo spirometric testing, were included this study. The spasticity derived from cervical spinal cord injury in four patients and cerebral palsy (CP) in two patients. Patients' Ashworth Scale scores and spirometer measurements obtained before and 1-6 months after the start of ITB therapy were evaluated and compared. RESULTS: Before ITB therapy, %FVC of all six patients was below 80%, and a restrictive respiratory disorder was diagnosed in five patients and a combined respiratory disorder in one patient. Ashworth Scale scores for both the lower and upper extremities improved significantly with ITB therapy. Forced vital capacity (FVC), %FVC, and forced expiratory volume at one sec also improved significantly with ITB therapy. CONCLUSIONS: Respiratory disorders were indeed present in our SCI and CP patients with spastic tetraplegia, and the respiratory function of these patients improved with ITB therapy. Our results suggest that ITB therapy is safe and efficacious in patients with spastic tetraplegia and respiratory dysfunction.

Is Phantom Limb Awareness Necessary for the Treatment of Phantom Limb Pain?

Phantom limb pain is attributed to abnormal sensorimotor cortical representations. Various feedback treatments have been applied to induce the reorganization of the sensorimotor cortical representations to reduce pain. We developed a training protocol using a brain-computer interface (BCI) to induce plastic changes in the sensorimotor cortical representation of phantom hand movements and demonstrated that BCI training effectively reduces phantom limb pain. By comparing the induced cortical representation and pain, the mechanisms worsening the pain have been attributed to the residual phantom hand representation. Based on our data obtained using neurofeedback training without explicit phantom hand movements and hand-like visual feedback, we suggest a direct relationship between cortical representation and pain. In this review, we summarize the results of our BCI training protocol and discuss the relationship between cortical representation and phantom limb pain. We propose a treatment for phantom limb pain based on real-time neuroimaging to induce appropriate cortical reorganization by monitoring cortical activities.

Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition.

Dynamic mode (DM) decomposition decomposes spatiotemporal signals into basic oscillatory components (DMs). DMs can improve the accuracy of neural decoding when used with the nonlinear Grassmann kernel, compared to conventional power features. However, such kernel-based machine learning algorithms have three limitations: large computational time preventing real-time application, incompatibility with non-kernel algorithms, and low interpretability. Here, we propose a mapping function corresponding to the Grassmann kernel that explicitly transforms DMs into spatial DM (sDM) features, which can be used in any machine learning algorithm. Using electrocorticographic signals recorded during various movement and visual perception tasks, the sDM features were shown to improve the decoding accuracy and computational time compared to conventional methods. Furthermore, the components of the sDM features informative for decoding showed similar characteristics to the high-γ power of the signals, but with higher trial-to-trial reproducibility. The proposed sDM features enable fast, accurate, and interpretable neural decoding.

Hippocampal sharp-wave ripples correlate with periods of naturally occurring self-generated thoughts in humans.

Core features of human cognition highlight the importance of the capacity to focus on information distinct from events in the here and now, such as mind wandering. However, the brain mechanisms that underpin these self-generated states remain unclear. An emerging hypothesis is that self-generated states depend on the process of memory replay, which is linked to sharp-wave ripples (SWRs), which are transient high-frequency oscillations originating in the hippocampus. Local field potentials were recorded from the hippocampus of 10 patients with epilepsy for up to 15 days, and experience sampling was used to describe their association with ongoing thought patterns. The SWR rates were higher during extended periods of time when participants' ongoing thoughts were more vivid, less desirable, had more imaginable properties, and exhibited fewer correlations with an external task. These data suggest a role for SWR in the patterns of ongoing thoughts that humans experience in daily life.

Accuracy of Boltless Frame-Based Stereo-Electroencephalography Electrode Implantation.

BACKGROUND AND OBJECTIVES: Boltless implantation of stereo-electroencephalography electrode is a useful alternative especially when anchor bolt is not available such as in country with limited resources or is less appropriate such as placement in patients with thin skull or at the occiput area, despite some drawbacks including potential dislodgement. While the accuracy of implantation using anchor bolt is well-studied, data on boltless implantation remain scarce. This study aimed to reveal the accuracy, permissible error for actual placement of electrodes within the grey matter, and delayed electrode dislodgement in boltless implantation. METHODS: A total of 120 electrodes were implanted in 15 patients using a Leksell Stereotactic G Frame with each electrode fixed on the scalp using sutures. Target point error was defined as the Euclidean distance between the planned target and the electrode tip on immediate postimplantation computed tomography. Similarly, delayed dislodgement was defined as the Euclidean distance between the electrode tips on immediate postimplantation computed tomography and delayed MRI. The factors affecting accuracy were evaluated using multiple linear regression. The permissible error was defined as the largest target point error that allows the maximum number of planned gray matter electrode contacts to be actually placed within the gray matter as intended. RESULTS: The median (IQR) target point error was 2.6 (1.7-3.5) mm, and the permissible error was 3.2 mm. The delayed dislodgement, with a median (IQR) of 2.2 (1.4-3.3) mm, was dependent on temporal muscle penetration (P = 5.0 × 10-4), scalp thickness (P < 5.1 × 10-3), and insertion angle (P = 3.4 × 10-3). CONCLUSION: Boltless implantation of stereo-electroencephalography electrode offers an accuracy comparable to those using anchor bolt. During the planning of boltless implantation, target points should be placed within 3.2 mm from the gray-white matter junction and a possible delayed dislodgement of 2.2 mm should be considered.

A deep learning model for the detection of various dementia and MCI pathologies based on resting-state electroencephalography data: A retrospective multicentre study.

Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise and accessible diagnostic methods continues, the efficacy of electroencephalography (EEG) combined with deep convolutional neural networks (DCNNs) in varied clinical settings remains unverified, particularly for pathologies underlying MCI such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and idiopathic normal-pressure hydrocephalus (iNPH). Addressing this gap, our study evaluates the generalizability of a DCNN trained on EEG data from a single hospital (Hospital #1). For data from Hospital #1, the DCNN achieved a balanced accuracy (bACC) of 0.927 in classifying individuals as healthy (n = 69) or as having AD, DLB, or iNPH (n = 188). The model demonstrated robustness across institutions, maintaining bACCs of 0.805 for data from Hospital #2 (n = 73) and 0.920 at Hospital #3 (n = 139). Additionally, the model could differentiate AD, DLB, and iNPH cases with bACCs of 0.572 for data from Hospital #1 (n = 188), 0.619 for Hospital #2 (n = 70), and 0.508 for Hospital #3 (n = 139). Notably, it also identified MCI pathologies with a bACC of 0.715 for Hospital #1 (n = 83), despite being trained on overt dementia cases instead of MCI cases. These outcomes confirm the DCNN's adaptability and scalability, representing a significant stride toward its clinical application. Additionally, our findings suggest a potential for identifying shared EEG signatures between MCI and dementia, contributing to the field's understanding of their common pathophysiological mechanisms.

Text and image generation from intracranial electroencephalography using an embedding space for text and images.

Objective.Invasive brain-computer interfaces (BCIs) are promising communication devices for severely paralyzed patients. Recent advances in intracranial electroencephalography (iEEG) coupled with natural language processing have enhanced communication speed and accuracy. It should be noted that such a speech BCI uses signals from the motor cortex. However, BCIs based on motor cortical activities may experience signal deterioration in users with motor cortical degenerative diseases such as amyotrophic lateral sclerosis. An alternative approach to using iEEG of the motor cortex is necessary to support patients with such conditions.Approach. In this study, a multimodal embedding of text and images was used to decode visual semantic information from iEEG signals of the visual cortex to generate text and images. We used contrastive language-image pretraining (CLIP) embedding to represent images presented to 17 patients implanted with electrodes in the occipital and temporal cortices. A CLIP image vector was inferred from the high-γpower of the iEEG signals recorded while viewing the images.Main results.Text was generated by CLIPCAP from the inferred CLIP vector with better-than-chance accuracy. Then, an image was created from the generated text using StableDiffusion with significant accuracy.Significance.The text and images generated from iEEG through the CLIP embedding vector can be used for improved communication.

Regulation of motor representation by phase-amplitude coupling in the sensorimotor cortex.

High-γ amplitude (80-150 Hz) represents motor information, such as movement types, on the sensorimotor cortex. In several cortical areas, high-γ amplitudes are coupled with low-frequency phases, e.g., α and θ (phase-amplitude coupling, PAC). However, such coupling has not been studied in the sensorimotor cortex; thus, its potential functional role has yet to be explored. We investigated PAC of high-γ amplitude in the sensorimotor cortex during waiting for and the execution of movements using electrocorticographic (ECoG) recordings in humans. ECoG signals were recorded from the sensorimotor cortices of 4 epilepsy patients while they performed three different hand movements. A subset of electrodes showed high-γ activity selective to movement type around the timing of motor execution, while the same electrodes showed nonselective high-γ activity during the waiting period (>2 s before execution). Cross frequency coupling analysis revealed that the high-γ amplitude during waiting was strongly coupled with the α phase (10-14 Hz) at the electrodes with movement-selective high-γ amplitudes during execution. This coupling constituted the high-γ amplitude peaking around the trough of the α oscillation, and its strength and phase were not predictive of movement type. As the coupling attenuated toward the timing of motor execution, the high-γ amplitude appeared to be released from the α phase to build a motor representation with phase-independent activity. Our results suggest that PAC modulates motor representation in the sensorimotor cortex by holding and releasing high-γ activity in movement-selective cortical regions.

Electrocorticographic control of a prosthetic arm in paralyzed patients.

OBJECTIVE: Paralyzed patients may benefit from restoration of movement afforded by prosthetics controlled by electrocorticography (ECoG). Although ECoG shows promising results in human volunteers, it is unclear whether ECoG signals recorded from chronically paralyzed patients provide sufficient motor information, and if they do, whether they can be applied to control a prosthetic. METHODS: We recorded ECoG signals from sensorimotor cortices of 12 patients while they executed or attempted to execute 3 to 5 simple hand and elbow movements. Sensorimotor function was severely impaired in 3 patients due to peripheral nervous system lesion or amputation, moderately impaired due to central nervous system lesions sparing the cortex in 4 patients, and normal in 5 patients. Time frequency and decoding analyses were performed with the patients' ECoG signals. RESULTS: In all patients, the high gamma power (80-150 Hz) of the ECoG signals during movements was clearly responsive to movement types and provided the best information for classifying different movement types. The classification performance was significantly better than chance in all patients, although differences between ECoG power modulations during different movement types were significantly less in patients with severely impaired motor function. In the impaired patients, cortical representations tended to overlap each other. Finally, using the classification method in real time, a moderately impaired patient and 3 nonparalyzed patients successfully controlled a prosthetic arm. INTERPRETATION: ECoG signals appear useful for prosthetic arm control and may provide clinically feasible motor restoration for patients with paralysis but no injury of the sensorimotor cortex.

Magnetoencephalographic neurofeedback training decreasesß-low-γphase-amplitude coupling of the motor cortex of healthy adults: a double-blinded randomized crossover feasibility study.

Objective.The coupling between the beta (13-30 Hz) phase and low gamma (50-100 Hz) amplitude in the motor cortex is thought to regulate motor performance. Abnormal phase-amplitude coupling (PAC) of beta-low gamma (ß-low-γPAC) is associated with motor symptoms of Parkinson's disease. However, the causal relationship betweenß-low-γPAC and motor performance in healthy subjects is unknown. We hypothesized that healthy subjects could change the strength of theß-low-γPAC in the resting state by neurofeedback training (NFT) to control theß-low-γPAC, such that the motor performance changes in accordance with the changes inß-low-γPAC in the resting state.Approach.We developed an NFT to control the strength of theß-low-γPAC in the motor cortex, which was evaluated by magnetoencephalography (MEG) using a current source estimation technique. Twenty subjects were enrolled in a double-blind randomized crossover trial to test the feasibility of the MEG NFT. In the NFT for 2 d, the subjects were instructed to reduce the size of a black circle whose radius was proportional (down-training) or inversely proportional (up-training) to the strength of theß-low-γPAC. The reaction times (RTs) to press a button according to some cues were evaluated before and after training. This study was registered at ClinicalTrials.gov (NCT03837548) and UMIN-CTR (UMIN000032937).Main results.Theß-low-γPAC during the resting state was significantly decreased after down-training, although not significantly after up-training. RTs tended to decrease after both trainings, however the differences were not statistically significant. There was no significant correlation between the changes inß-low-γPAC during rest and RTs.Significance.The proposed MEG NFT was demonstrated to change theß-low-γPAC of the motor cortex in healthy subjects. However, a relationship between PAC and RT has not yet been demonstrated.

Clinical Factors Related to Outcomes in Pediatric Epilepsy Surgery: Insight into Predictors of Poor Surgical Outcome.

Successful surgery for drug-resistant pediatric epilepsy can facilitate motor and cognitive development and improve quality of life by resolution or reduction of epileptic seizures. Therefore, surgery should be considered early in the disease course. However, in some cases, the estimated surgical outcomes are not achieved, and additional surgical treatments are considered. In this study, we investigated the clinical factors related with such unsatisfactory outcomes.We reviewed the clinical data of 92 patients who underwent 112 surgical procedures (69 resection and 53 palliation procedures). Surgical outcomes were assessed according to the postoperative disease status, which was classified as good, controlled, and poor. The following clinical factors were analyzed in relation to surgical outcome: sex, age at onset, etiology (malformation of cortical development, tumor, temporal lobe epilepsy, scar, inflammation, and non-lesional epilepsy), presence of genetic cause, and history of developmental epileptic encephalopathy. At a median of 59 (30-81.25) months after the initial surgery, the disease status was good in 38 (41%), controlled in 39 (42%), and poor in 15 (16%) patients. Among the evaluated factors, etiology exhibited the strongest correlation with surgical outcomes. Tumor-induced and temporal lobe epilepsy were correlated with good, whereas malformation of cortical development, early seizure onset, and presence of genetic cause were correlated with poor disease status. Although epilepsy surgery for the patients who present with the latter factors is challenging, these patients demonstrate a greater need for surgical treatment. Hence, development of more effective surgical options is warranted, including palliative procedures.

Characteristics of Changes in Intrathecal Baclofen Dosage over Time due to Causative Disease.

Intrathecal baclofen (ITB) therapy effectively treats spasticity caused by brain or spinal cord lesions. However, only a few studies compare the course of treatment for different diseases. We investigated the change in daily dose of baclofen per year and its associated adverse events in patients presenting with the three most common etiologies at our institute: hereditary spastic paraplegia, cerebral palsy, and spinal cord injury. The ITB pumps were implanted from July 2007 to August 2019, with a mean follow-up period of 70 months. In patients with hereditary spastic paraplegia, baclofen dosage was reduced after eight years following ITB introduction, and the treatment was terminated in one patient owing to disease progression. In patients with cerebral palsy, the dosage increased gradually, and became constant in the 11th year. Patients with spinal cord injury gradually increased their baclofen dosage throughout the entire observation period. Severity and adverse event rates were higher in patients with cerebral palsy than in others. The degree and progression of spasticity varied depending on the causative disease. Understanding the characteristics and natural history of each disease is important when continuing ITB treatment.

Beta rhythmicity in human motor cortex reflects neural population coupling that modulates subsequent finger coordination stability.

Human behavior is not performed completely as desired, but is influenced by the inherent rhythmicity of the brain. Here we show that anti-phase bimanual coordination stability is regulated by the dynamics of pre-movement neural oscillations in bi-hemispheric primary motor cortices (M1) and supplementary motor area (SMA). In experiment 1, pre-movement bi-hemispheric M1 phase synchrony in beta-band (M1-M1 phase synchrony) was online estimated from 129-channel scalp electroencephalograms. Anti-phase bimanual tapping preceded by lower M1-M1 phase synchrony exhibited significantly longer duration than tapping preceded by higher M1-M1 phase synchrony. Further, the inter-individual variability of duration was explained by the interaction of pre-movement activities within the motor network; lower M1-M1 phase synchrony and spectral power at SMA were associated with longer duration. The necessity of cortical interaction for anti-phase maintenance was revealed by sham-controlled repetitive transcranial magnetic stimulation over SMA in another experiment. Our results demonstrate that pre-movement cortical oscillatory coupling within the motor network unknowingly influences bimanual coordination performance in humans after consolidation, suggesting the feasibility of augmenting human motor ability by covertly monitoring preparatory neural dynamics.

Abnormal phase-amplitude coupling characterizes the interictal state in epilepsy.

Objective.Diagnosing epilepsy still requires visual interpretation of electroencephalography (EEG) and magnetoencephalography (MEG) by specialists, which prevents quantification and standardization of diagnosis. Previous studies proposed automated diagnosis by combining various features from EEG and MEG, such as relative power (Power) and functional connectivity (FC). However, the usefulness of interictal phase-amplitude coupling (PAC) in diagnosing epilepsy is still unknown. We hypothesized that resting-state PAC would be different for patients with epilepsy in the interictal state and for healthy participants such that it would improve discrimination between the groups.Approach.We obtained resting-state MEG and magnetic resonance imaging (MRI) in 90 patients with epilepsy during their preoperative evaluation and in 90 healthy participants. We used the cortical currents estimated from MEG and MRI to calculate Power in theδ(1-3 Hz),θ(4-7 Hz),α(8-13 Hz),ß(13-30 Hz), lowγ(35-55 Hz), and highγ(65-90 Hz) bands and FC in theθband. PAC was evaluated using the synchronization index (SI) for eight frequency band pairs: the phases ofδ, θ, α, andßand the amplitudes of low and highγ. First, we compared the mean SI values for the patients with epilepsy and the healthy participants. Then, using features such as PAC, Power, FC, and features extracted by deep learning (DL) individually or combined, we tested whether PAC improves discrimination accuracy for the two groups.Main results.The mean SI values were significantly different for the patients with epilepsy and the healthy participants. The SI value difference was highest forθ/lowγin the temporal lobe. Discrimination accuracy was the highest, at 90%, using the combination of PAC and DL.Significance.Abnormal PAC characterized the patients with epilepsy in the interictal state compared with the healthy participants, potentially improving the discrimination of epilepsy.

Frequency band coupling with high-frequency activities in tonic-clonic seizures shifts from θ to δ band.

OBJECTIVE: To clarify variations in the relationship between high-frequency activities (HFAs) and low-frequency bands from the tonic to the clonic phase in focal to bilateral tonic-clonic seizures (FBTCS), using phase-amplitude coupling. METHODS: This retrospective study enrolled six patients with drug-resistant focal epilepsy who underwent intracranial electrode placement at Osaka University Hospital (July 2018-July 2019). We recorded 11 FBTCS. The synchronization index (SI) and receiver-operating characteristic (ROC) analysis were used to analyze the coupling between HFA amplitude (80-250 Hz) and lower frequencies phase. RESULTS: In the tonic phase, the θ (4-8 Hz)-HFA coupling peaked, and the HFA power occurred at baseline (0 µV) of θ oscillations. In contrast, in the clonic phase, the δ (2-4 Hz)-HFA coupling peaked, and the HFA power occurred at the trough of δ oscillations. ROC analysis indicated that the δ-HFA SI discriminated well the clonic from the tonic phase. CONCLUSIONS: The main low-frequency band modulating the HFA shifted from the θ band in the tonic phase to the δ band in the clonic phase. SIGNIFICANCE: Neurophysiological key frequency bands were implied to be the θ band and δ band in tonic and clonic seizures, respectively, which improves our understanding of FBTCS.

Altered Thalamic Connectivity Due to Focused Ultrasound Thalamotomy in Patients with Essential Tremor.

BACKGROUND: Although stereotactic ablation surgery is known to ameliorate involuntary movement dramatically, little is known regarding alterations in whole-brain networks due to disruption of the deep brain nucleus. To explore changes in the whole-brain network after thalamotomy, we analyzed structural and functional connectivity alterations using resting-state functional magnetic resonance imaging and diffusion tensor imaging in patients with essential tremor who had undergone focused ultrasound (FUS) thalamotomy. METHODS: Seven patients with intractable essential tremors and 7 age-matched healthy controls were enrolled in the study. The tremor score in essential tremor patients was assessed, and resting-state functional magnetic resonance imaging and diffusion tensor imaging were performed before and 3 months after left ventral intermediate nucleus thalamotomy using FUS. RESULTS: There was a significant improvement in the tremor of the right hand after FUS thalamotomy. Seed-based functional connectivity analysis revealed a significant increase in functional connectivity between the left thalamus and the caudal part of the dorsal premotor cortex after FUS thalamotomy. Structural connectivity analysis did not detect statistically significant changes between before and after FUS. There was no correlation between the changes in functional connectivity and tremor score. CONCLUSIONS: Although the number of cases is small, our results show that functional connectivity between the thalamus and the premotor cortex increases after the amelioration of tremors by FUS thalamotomy. The lack of correlation between increased functional connectivity and clinical tremor scores suggests that the observed increase in functional connectivity may be a compensatory change in the secondary sensorimotor changes that occur after thalamotomy.

Neurofeedback Training without Explicit Phantom Hand Movements and Hand-Like Visual Feedback to Modulate Pain: A Randomized Crossover Feasibility Trial.

Phantom limb pain is attributed to abnormal sensorimotor cortical representations, although the causal relationship between phantom limb pain and sensorimotor cortical representations suffers from the potentially confounding effects of phantom hand movements. We developed neurofeedback training to change sensorimotor cortical representations without explicit phantom hand movements or hand-like visual feedback. We tested the feasibility of neurofeedback training in fourteen patients with phantom limb pain. Neurofeedback training was performed in a single-blind, randomized, crossover trial using two decoders constructed using motor cortical currents measured during phantom hand movements; the motor cortical currents contralateral or ipsilateral to the phantom hand (contralateral and ipsilateral training) were estimated from magnetoencephalograms. Patients were instructed to control the size of a disk, which was proportional to the decoding results, but to not move their phantom hands or other body parts. The pain assessed by the visual analogue scale was significantly greater after contralateral training than after ipsilateral training. Classification accuracy of phantom hand movements significantly increased only after contralateral training. These results suggested that the proposed neurofeedback training changed phantom hand representation and modulated pain without explicit phantom hand movements or hand-like visual feedback, thus showing the relation between the phantom hand representations and pain. PERSPECTIVE: Our work demonstrates the feasibility of using neurofeedback training to change phantom hand representation and modulate pain perception without explicit phantom hand movements and hand-like visual feedback. The results enhance the mechanistic understanding of certain treatments, such as mirror therapy, that change the sensorimotor cortical representation.

Tight adhesions after spinal cord stimulation observed during dorsal root entry zone lesioning for pain after spinal root avulsion: illustrative cases.

BACKGROUND: Patients often experience strong shooting pains after spinal root avulsion. The efficacy of spinal cord stimulation (SCS) for this type of pain is inconsistent; however, dorsal root entry zone (DREZ) lesioning (DREZ-lesion) has often proven to be an effective treatment modality. The authors report two cases in which DREZ-lesion was performed to treat pain after spinal root avulsion after implantation of SCS, but the operations were challenging due to strong adhesions. OBSERVATIONS: The authors present two cases of patients with pain after spinal root avulsion in whom SCS implantation was only temporarily effective. Patients complained of persistent and paroxysmal shooting pains in the upper extremities. SCS removal and DREZ-lesion were performed, but adhesions in the epidural and subdural space contacting the leads were strong, making it difficult to expose the DREZ. LESSONS: Although adhesions around the spinal cord can be caused by trauma, the authors believe that in these cases, the adhesions could have been caused by the SCS leads. There are few previous reports confirming the efficacy of SCS in treating pain after spinal root avulsion; therefore, caution is required when considering SCS implantation.